A Predictive Command Governor-Based Adaptive Cruise Controller With Collision Avoidance for Non-Connected Vehicle Following

• Published in: IEEE transactions on intelligent transportation systems Vol. 23; no. 8; pp. 12276 - 12286
• Main Authors: Groelke, Ben, Earnhardt, Christian, Borek, John, Vermillion, Chris
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.08.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptive control; Advanced driver assistance systems (ADAS); Biological system modeling; Collision avoidance; collision avoidance (CA); Constraints; Controllers; Cruise control; Data models; Deceleration; Engineering; Fuel economy; Heavy duty trucks; Lead; Lower bounds; predictive control; Safety; Simulation; Statistical analysis; Transportation; Vehicles; Velocity; Velocity distribution
• ISSN: 1524-9050; 1558-0016
• DOI: 10.1109/TITS.2021.3112113

This paper presents a command governor (CG) based adaptive cruise controller (ACC) that is applied in simulation to normal driving scenarios and emergency stopping scenarios. The vehicle-following case study used in this paper involves a heavy-duty ego vehicle and a light-duty non-connected lead vehicle (i.e., the ego vehicle does not communicate with the lead vehicle and can only infer the lead vehicles' position and velocity states through its own sensors). Typically, to ensure constraints in the presence of disturbances, receding horizon based ACCs will assume some known worst-case behavior of the lead vehicle. In the presence of a stochastic, non-connected lead vehicle, however, achieving such a guarantee requires a worst-case assumption on the behavior of the lead vehicle for all future time. In this work, the CG assumes a lead vehicle velocity profile that will be achieved with a prescribed level of certainty, based on a stochastic characterization of lead vehicle behavior that has been informed by actual on-road data. The CG ensures safe following distance under this probabilistic lead vehicle assumption. Here, "safe following distance" is based on the ego vehicle's ability to come to a stop without collision if the lead vehicle were to suddenly brake at maximum deceleration after proceeding at a velocity profile that is prescribed based on a statistical lower bound on lead vehicle velocity. Ultimately, the CG ensures that the worst-case safe following distance is satisfied with a prescribed probability, thereby paralleling chance-constrained CG formulations. Simulation results for a heavy-duty truck indicate that the CG-based ACC outperforms a PID-ACC in terms of fuel economy and drivability. Additionally, the CG-ACC approach was able to ensure rear-end collision avoidance in emergency stopping simulations.